Interactive detection of incrementally learned concepts in images with ranking and semantic query interpretation

This research was performed in the GOOSE project, which is jointly funded by the MIST research program of the Dutch Ministry of Defense and the AMSN enabling technology program.The number of networked cameras is growing exponentially. Multiple applications in different domains result in an increasing need to search semantically over video sensor data. In this paper, we present the GOOSE demonstrator, which is a real-time general-purpose search engine that allows users to pose natural language queries to retrieve corresponding images. Top-down, this demonstrator interprets queries, which are presented as an intuitive graph to collect user feedback. Bottomup, the system automatically recognizes and localizes concepts in images and it can incrementally learn novel concepts. A smart ranking combines both and allows effective retrieval of relevant images.peer-reviewe

Serum lipids, apoproteins and nutrient intake in rural Cretan boys consuming high-olive-oil diets

A high intake of olive oil has produced high levels of high-density and low levels of low-density lipoprotein cholesterol in short-term dietary trials. To investigate long-term effects of olive oil we have studied the diet and serum lipids of boys in Crete, where a high olive oil consumption is the norm. Seventy-six healthy rural Cretan boys aged 7–9 years were studied. The diet was assessed by a 2-day dietary recall. Blood was collected according to a standardized protocol and sera were analyzed in a rigidly standardized laboratory. The mean daily intake of energy was 11.0 MJ (2629 kcal). The intake of fat (45.0% of energy) and oleic acid (27.2% of energy) was high, and that of saturated fat low (10.0% of energy), reflecting a high consumption of olive oil. The high consumption of olive oil was confirmed by a high proportion of oleic-acid (27.1 %) in serum cholesteryl fatty acids. Mean concentration of serum total cholesterol was 4.42 mmol 1−1 (171 mg dl−1 ), of HDL-cholesterol 1.40 mmol 1−1 (54 mg dl−1), of serum triglycerides 0.59 mmol I−1 (52 mg dl−1 ), of apo-A1 1210 mg 1−1 and of LDL apo-B 798 mg 1−1. The body mass index of the Cretan boys (18.2 kg m−2) was on average 2 kg m−2 higher than that of boys from other countries. Contrary to our expectation, the Cretan boys did not show a more favourable serum lipoprotein pattern than boys from more westernized countries studied previously using the same protocol. Our hypothesis that a typical, olive-oil-rich Cretan diet causes a relatively high HDL- to total cholesterol ratio is not supported by the present findings

HIV-1 Disease Progression Is Associated with Bile-Salt Stimulated Lipase (BSSL) Gene Polymorphism

Background: DC-SIGN expressed by dendritic cells captures HIV-1 resulting in trans-infection of CD4+ T-lymphocytes. However, BSSL (bile-salt stimulated lipase) binding to DC-SIGN interferes with HIV-1 capture. DC-SIGN binding properties of BSSL associate with the polymorphic repeated motif of BSSL exon 11. Furthermore, BSSL binds to HIV-1 co-receptor CXCR4. We hypothesized that BSSL modulates HIV-1 disease progression and emergence of CXCR4 using HIV-1 (X4) variants. Results: The relation between BSSL genotype and HIV-1 disease progression and emergence of X4 variants was studied using Kaplan Meier and multivariate Cox proportional hazard analysis in a cohort of HIV-1 infected men having sex with men (n = 334, with n = 130 seroconverters). We analyzed the association of BSSL genotype with set-point viral load and CD4 cell count, both pre-infection and post-infection at viral set-point. The number of repeats in BSSL exon 11 were highly variable ranging from 10 to 18 in seropositive individuals and from 5-17 in HRSN with 16 repeats being dominant (>80% carry at least one allele with 16 repeats). We defined 16 to 18 repeats as high (H) and less than 16 repeats as low (L) repeat numbers. Homozygosity for the high (H) repeat number BSSL genotype (HH) correlated with high CD4 cell numbers prior to infection (p = 0.007). In HIV-1 patients, delayed disease progression was linked to the HH BSSL genotype (RH = 0.462 CI = 0.282-0.757, p = 0.002) as was delayed emergence of X4 variants (RH = 0.525, 95% CI = 0.290-0.953, p = 0.034). The LH BSSL genotype, previously found to be associated with enhanced DC-SIGN binding of human milk, was identified to correlate with accelerated disease progression in our cohort of HIV-1 infected MSM (RH = 0.517, 95% CI = 0.328-0.818, p = 0.005). Conclusion: We identify BSSL as a marker for HIV-1 disease progression and emergence of X4 variants. Additionally, we identified a relation between BSSL genotype and CD4 cell counts prior to infectio

Simulation solution validation for an integrated emergency post

Simulation solution validation concerns the comparison between the expected and actual performance of a solution provided by a simulation model. Such a comparison might become challenging when not only the implementation of the solution changed the environment, but also the processes and data have changed. We illustrate this challenge using a case study at an Integrated Emergency Post (IEP), which is a collaboration between a general practitioners post and a hospital's emergency department to provide out-of-hours emergency care. After performing a simulation study, our solution has been implemented, after which data has been gathered for two years. We validated the solution by performing various comparisons, using simulated and realized performance, under the original and changed data and processes, and with and without the proposed solution. We propose a solution validation framework to structure these comparisons, and provide key insights regarding solution validation, using our case study at the IEP

Usage impact on data center electricity needs: A system dynamic forecasting model

This article presents a forecasting model of data center electricity needs based on understanding usage growth and we conclude that this growth is not fully compensated by efficiency gains of data center technological innovations. We predict a combined growth of data center electricity needs of 286 TWh in 2016 until about 321 TWh in 2030, if all currently known growth factors remain the same. We next run simulations for the end of Moore’s law and the growth of industrial Internet of Things (IoT). The end of Moore’s law results in about 658 TWh for 2030 and an increase of the share of global data center electricity consumption from about 1.15% in 2016 to 1.86% in 2030. A rise of the Industrial IoT may result into total energy consumption of about 364 TWh (about 1.03%) in 2030. Moore’s law and IoT combined cause data center energy needs going up to 752 TWh in 2030, and about 2.13% of global electricity available. Our sensitivity analysis reveals that the future impact of the data centers’ electricity consumption is vulnerable to behavioral usage trends, since the 95% confidence interval of [343, 1031] TWh is relatively wide. Our forecasts, however, exclude the energy needs of mobile devices, edge and fog computing. We offer a system dynamic model and simulation input data selected from the existing literature for replicating this study and applying alternative parameters to it. We further suggest multiple research directions on usage impact on data center energy consumption